A deep learning-based emulator of the regional atmospheric model MAR for estimation of the Antarctic surface mass balance

Faithful modeling of Antarctic climate relies on capturing polar-specific processes at high spatial resolution (~10-30 km). Most CMIP earth system models (ESMs) used for climate projections inadequately represent physical processes that are key drivers in polar climates, and operate at resolutions too coarse to resolve them. Polar-oriented regional climate models (RCMs) are considered the state-of-the-art in modeling the atmosphere at high latitudes, where air-snow interactions are critical, but they are costly to run. This limits their use for exploration of large ensembles and scenarios as well as their potential of integrating into a coupled modeling pipeline.

In order to address these limitations, we develop an affordable surrogate model, or emulator, of the polar-oriented Modèle Atmosphérique Régional (MAR), using deep learning and a variant of the commonly used U-Net convolutional neural network architecture. The emulator is trained to predict 35 km-resolution daily maps of surface mass balance (SMB) components—snowfall, rainfall, run-off and sublimation—over the Antarctic ice sheet from large-scale atmospheric fields of ESMs, effectively learning the downscaling function embedded in MAR. To achieve this, we use a dataset composed of MAR simulations forced by 4 CMIP ESMs over the 1980–2100 period, covering SSPs 1-2.6, 2-4.5 and 5-8.5. We conduct different experiments to assess its best-case performance as well as its transferability to unseen scenarios and ESMs.

The emulator demonstrates strong in-domain skill, displaying high fidelity in reproducing both day-to-day and spatial synoptic variability of the predicted quantities. Long-term SMB trends and interannual variability through 2100 are also well-replicated, with predicted integrated surface mass change over the 1980–2100 period differing by only 1% from MAR. We find that the emulator is robust against unseen emission scenarios, with marginal increase of up to few percent in RMSE. Transferability to other ESMs proves more challenging but results remain promising.

The MAR emulator can be used to generate SMB forcings for ice-sheet models at a negligible computational cost compared to RCMs, allowing century-scale simulations to be produced within minutes and thereby enabling the exploration of a wide range of scenarios and ensemble members. We suggest the general framework of this work could allow for the emulation of MAR in any application where it can be traditionally used. Ongoing work is also investigating the applicability of the emulator within an atmosphere–ice sheet coupled framework.